جست‌وجوی استاد و پژوهشگر

Rachel Rudingerمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning and Data Science
• AI and Robotics
• +۱ مورد دیگر

Rachel Rudinger is an Assistant Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Her research focuses on Natural Language Processing (NLP), Machine Learning, and AI ethics, particularly addressing sociocultural biases and fairness in large language models (LLMs). She holds a PhD from Johns Hopkins University (2019) and a B.S. from Yale University (2013). Rudinger's work explores equitable cultural alignment in AI systems, common ground misalignment in dialog systems, and the mutual influence of gender and occupation in LLMs. She received the NSF CAREER Award in 2024 for her project on robust, fair, and culturally aware commonsense reasoning. Her recent publications investigate empathy gaps in LLMs, synthetic data effectiveness in disaster response, and bias measurement techniques across domains. As an advisor, she guides seven PhD students including Christabel Acquaye and Haozhe An. Her research spans diverse topics from legal language analysis to maternal health question answering, reflecting her commitment to interdisciplinary AI ethics. She actively contributes to workshops on commonsense representation and serves as a reviewer for top conferences in NLP and AI.

Adrian Lewمشاهده پروفایل

استاد

• Computational Solid Mechanics
• Numerical Algorithms
• Hydraulic Fracturing Simulation
• +۳ مورد دیگر

Adrian Lew is a Professor of Mechanical Engineering at Stanford University, specializing in computational solid mechanics and numerical algorithms. His research focuses on hydraulic fracturing simulation, embedded boundary methods, and material model design. He holds a PhD in Mechanical Engineering from Caltech (2003). His work bridges advanced numerical techniques with real-world applications in geophysics, material science, and structural engineering. Education: PhD, Mechanical Engineering, California Institute of Technology, 2003 Research Interests: Lew's group develops algorithms for time-integration embedded boundary methods and hydraulic fracturing simulations. Key areas include curvilinear crack propagation, universal meshing for complex geometries, and high-fidelity fracture mechanics. His work on variational integrators and discontinuous Galerkin methods has advanced computational efficiency in nonlinear elasticity and thermodynamics. Publications: Recent articles emphasize mesh optimization (DVRlib), fracture path instabilities, and magma chamber dynamics. His methodologies address challenges in 3D crack modeling, fluid-structure interaction, and high-order approximations in domains with singularities. Advising & Grants: Lew's research is supported by projects in computational geophysics and material science. Though no advisees are listed, his work involves collaborative teams focused on algorithmic innovation and high-performance computing.

Ying Chenمشاهده پروفایل

دانشیار

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

• Educational Technology
• Human-Computer Interaction
• Data Science
• +۴ مورد دیگر

Prof. April Wang is an Assistant Professor in the Department of Computer Science at ETH Zurich. Her work focuses on Educational Technology, Human-Computer Interaction, and AI-driven tools for programming education. She is affiliated with the Professur Educational Technology group and teaches courses such as Human Computer Interaction and Ethics in Computing. Her research explores interactive learning environments, collaborative data science workflows, and AI-augmented educational systems. Her research interests span computational notebooks, live coding interfaces, and human-AI collaboration in software development. Notable contributions include frameworks like DBox for algorithm learning and EDBooks for interactive programming narratives. She also investigates challenges in real-time collaboration tools and automated documentation systems for data scientists. Recent work emphasizes understanding user needs in automated copilot systems and resolving conflicts in collaborative notebook environments. Her projects often bridge theoretical HCI principles with practical educational tools, aiming to enhance both instructor efficacy and student engagement. Prof. Wang’s teaching portfolio includes courses on user-centered interface design and ethical scientific practices. She actively contributes to advancing interdisciplinary computational literacy through initiatives like datAR, which uses everyday objects to teach data concepts.

Jeff Schneiderمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Autonomous Systems
• Reinforcement Learning
• +۷ مورد دیگر

Jeff Schneider is a Research Professor at the Robotics Institute within the School of Computer Science at Carnegie Mellon University. His research focuses on active learning, data mining, reinforcement learning, optimization, and intelligent control , applied to industrial and commercial challenges. Current PhD Students : Anoushka Alavilli, Benjamin Freed, Tejus Gupta, Albert Xu, Brian Yang Current Masters Students : Wen-Tse Chen, Xintong Duan, Aman Mehra, Vedant Mundheda, Zhouchonghao Wu Past PhD Students : J. Andrew Bagnell, Viraj Mehta, Matthew Tesch Past Masters Students : Ravi Tej Akella, Swapnil Pande, Siddharth Venkatraman Schneider's research bridges machine learning and autonomous systems , particularly in reinforcement learning , multi-robot coordination , and self-driving car technology . His work emphasizes practical applications of learning algorithms in real-world scenarios. His recent publications highlight advancements in offline reinforcement learning , multi-agent policy coordination , and behavior planning for autonomous vehicles . These studies often integrate deep learning and probabilistic modeling to address complex control and decision-making problems. Labs: Auton Lab CMU Center for Autonomous Vehicle Research Consulting & Industry Impact: Schneider has consulted for organizations like Uber ATG, Psychogenics, and Schenley Park Research, applying machine learning to domains such as self-driving cars , nuclear fusion , marketing , and drug discovery .

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

Ian Davidsonمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Constraint Programming
• +۶ مورد دیگر

Professor Ian Davidson is a faculty member in the Department of Computer Science at the University of California Davis, College of Engineering. His research focuses on machine learning, data mining, and constraint programming, with applications in neuroscience, healthcare, and social networks. He emphasizes rigorous algorithm design and human-in-the-loop learning paradigms. Editorial Board Member: ACM TKDD, IEEE TKDE, Springer DMKD Conference Leadership: PC Chair (SDM 2012), Vice/Area Chair (IEEE ICDM, ACM KDD, SIAM DM, ECML/PKDD 2013-2015) Research Interests: Human-in-the-loop learning (active, transfer, and transductive frameworks) Constraint programming and spectral methods for clustering and classification Applications in neuroimaging analysis, intelligent tutoring systems, and social impact domains Fairness in machine learning and clustering algorithms Tensor decomposition and matrix factorization techniques Interdisciplinary collaborations in neuroscience and healthcare Recent publications highlight his work on fairness-aware clustering with constraint programming, advanced spectral methods for brain connectivity analysis, and explainable AI frameworks. His research often combines theoretical rigor with practical applications in clinical domains. Scientific Awards: Best Paper Award, SIAM Data Mining Conference 2005 Best Paper Award, ECML/PKDD 2006 Best Paper Award, ICDM 2006 Students & Collaborators: Former students: Xiang Wang (IBM Watson), Buyue Qian (Xi'an Jiaotong University), Tom Kuo (Google), Sean Gilpin (Google) Current advisees: Aubrey Guess, Zilong Bai, Erin McGinnis, Zheng Fang, Hongjing Zhang

• Decision Analysis
• Operations
• Information Systems
• +۷ مورد دیگر

John C. Butler is a Clinical Associate Professor in the Finance Department at the McCombs School of Business, University of Texas at Austin. He holds leadership roles as Academic Director of the Kay Bailey Hutchison Energy Center, Director of the MS Finance Program, and Director of the Energy Management Minor. His academic journey includes a PhD in Management Science and Information Systems from UT Austin (1998) and a BBA from Texas A&M University (1991). His research focuses on applications of decision analysis across domains including operations, finance, and information systems. Key areas include risk analysis, optimization, multi-attribute utility theory, and energy finance. His work integrates theoretical modeling with empirical validation to address complex decision-making challenges in both public and private sectors. Butler's publications demonstrate a consistent focus on decision modeling methodologies, with recent work emphasizing risk quantification and utility theory applications. His articles frequently intersect operations research, behavioral economics, and systems optimization, reflecting interdisciplinary approaches to solving managerial and policy problems. Awards and Honors: MBA Applause Award (2008, 2011) Finalist, INFORMS Franz Edelman Award (2004) INFORMS Decision Analysis Society Practice Award (2000) Fred Moore Teaching Award Dean's Research Fellowship, Ohio State University (2004) Leadership & Advising: Butler has supervised 11 PhD students to completion and secured significant grants including DOE funding for nuclear terrorism risk analysis. He directs multiple energy finance initiatives and serves on editorial boards for Decision Analysis and previously Decision Support Systems . Centers & Programs: As Academic Director of the Kay Bailey Hutchison Energy Center, he leads interdisciplinary energy research. He also developed the Energy Finance concentration and redesigned the MS Finance curriculum to incorporate quantitative energy market analysis.

• Higher-order finite element methods
• Numerical linear algebra
• Multigrid methods
• +۳ مورد دیگر

Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

Dan Jiaoمشاهده پروفایل

استاد

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +۸ مورد دیگر

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team

• Algebra
• Combinatorics
• Number Theory
• +۳ مورد دیگر

Gábor Somlai is a mathematics researcher at Eötvös Loránd University's Faculty of Science, affiliated with the Department of Algebra and Number Theory since 2006. He is a core member of the HUN-REN-ELTE Geometric and Algebraic Combinatorics Research Group (previously MTA-ELTE until 2023) and participates in the Asymptotic Group Theory ERC project. His institutional roles include contributions to the Mathematics Doctoral School and the Institute of Mathematics. His research spans Algebra , Combinatorics , and Number Theory , with emphasis on spectral sets, tiling problems, group theory, and finite geometry. Recent work explores cyclotomic polynomials, the generalized trifference problem, and connections between tiling and spectral properties in finite abelian groups. His publications reveal a strong focus on combinatorial number theory and algebraic methods in discrete geometry. Somlai's publication trends show sustained contributions to theoretical foundations of tiling and spectral sets (testing Fuglede's conjecture in cyclic groups) Cayley graph properties and CI-group classifications polynomial methods in finite geometry and direction problems His work bridges pure algebra with combinatorial applications, often resolving long-standing conjectures in specialized domains. As part of the HUN-REN-ELTE research group, he collaborates on geometric and algebraic combinatorics projects, leveraging ERC funding for asymptotic group theory investigations. His research infrastructure includes access to the Alfréd Rényi Institute of Mathematics (http://www.renyi.hu) resources.

Xu Jinchaoمشاهده پروفایل

استاد

• Multigrid methods
• Machine learning
• Finite element methods
• +۵ مورد دیگر

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

• Numerical Analysis
• Computational Electromagnetism
• Finite Element Methods
• +۳ مورد دیگر

Ana Maria Alonso Rodriguez is a Full Professor of Numerical Analysis at the Department of Mathematics, University of Trento. She holds a PhD in Applied Mathematics from Universidad Complutense de Madrid (1993) and has held academic positions across Italy and Spain since 1990. Her research focuses on numerical methods for partial differential equations, computational electromagnetism, finite element methods, and domain decomposition techniques. She has organized international workshops and minisymposia, including the 2022 Oberwolfach workshop on Hilbert Complexes and the 2018 ICOSAHOM conference session on high-order methods. Her work bridges numerical analysis, topology, and applied electromagnetism, with recent contributions to Whitney finite elements and discrete potential theory. Education: PhD in Applied Mathematics, Universidad Complutense de Madrid (1988-1993) Licenciatura en Ciencias Matematicas, same institution (1982-1987) Research emphasizes high-order discretizations for electromagnetic problems, leveraging finite element exterior calculus and graph-based decomposition techniques. Recent work (2024) advances tree-cotree methods for curl operator spectra and Whitney form interpolation. She actively collaborates with international institutions like the CI2MA in Chile and the Laboratoire J. A. Dieudonné in France. Teaching includes courses on numerical PDEs, finite elements, computational electromagnetism, and MATLAB-based numerical analysis at both undergraduate and PhD levels. She has supervised numerous courses in Italy and Spain since 2000, integrating practical software tools like FreeFem and MODULEF into instruction.

Josef Dickمشاهده پروفایل

استاد

• Numerical Integration
• Quasi-Monte Carlo Rules
• Number Theory
• +۵ مورد دیگر

Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.